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Fact Checking on Knowledge Graphs

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Data Science for Fake News

Part of the book series: The Information Retrieval Series ((INRE,volume 42))

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Abstract

Fact checking, which verifies whether a given statement is true, could play a vital role in fake news detection. For example, for a given piece of news, a potential solution could involve a series of steps, including extracting statements from the news via text parsing, checking the validity of the extracted statements (i.e., fact checking), and classifying the news as fake if some statements have been confirmed to be false and performing further fake news detection processes otherwise. Considering that knowledge graphs are a popular way of representing knowledge, which could be used for verifying or counter-verifying statements, several solutions have been proposed that make use of knowledge graphs for fact checking. In this chapter, recent studies on fact checking with the help of knowledge graphs are reviewed, and three representative solutions, namely, Knowledge Linker, PredPath, and Knowledge Stream, are introduced with some details. Specifically, Knowledge Linker utilizes the semantic proximity metrics for mining knowledge graphs, PredPath employs the link prediction method and introduces a newly defined metric, and Knowledge Stream models the fact-checking problem as an optimization problem and uses flow theory for solving the problem.

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Authors and Affiliations

  1. Wuhan University, Wuhan, China

    Weichen Luo

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Cheng Long

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  1. Weichen Luo
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  2. Cheng Long
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Luo, W., Long, C. (2021). Fact Checking on Knowledge Graphs. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-62696-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-62696-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62695-2

  • Online ISBN: 978-3-030-62696-9

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